Prevalence and determinants of eczema among females aged 21 to 32 years in Jeddah city – Saudi Arabia

Binyamin, Sultana T.; Algamal, Fathi; Yamani, Amani N.; Labani, Mai S.; Alaqbi, Fatenah H.; Baeshen, Anhar A.; Binyamin, Abdulelah T.; Al-Nazzawi, Mohammed T.; Alamoudi, Shahad M.; Aljunaid, Mohammed; Basaeed, Lamis F.; Alhasan, Ahmed H.; Al-Marzoug, Alanoud

doi:10.7241/ourd.20171.5

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…We focus on these diagnoses since they are most common in the Middle East and Saudi Arabia. [29][30][31][32]…”

Section: Materials and Methods Datasetmentioning

confidence: 99%

Deep Learning Based Classification of Dermatological Disorders

AlSuwaidan

2023

Biomed Eng Comput Biol

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Automated medical diagnosis has become crucial and significantly supports medical doctors. Thus, there is a demand for inventing deep learning (DL) and convolutional networks for analyzing medical images. Dermatology, in particular, is one of the domains that was recently targeted by AI specialists to introduce new DL algorithms or enhance convolutional neural network (CNN) architectures. A significantly high proportion of studies in the field are concerned with skin cancer, whereas other dermatological disorders are still limited. In this work, we examined the performance of 6 CNN architectures named VGG16, EfficientNet, InceptionV3, MobileNet, NasNet, and ResNet50 for the top 3 dermatological disorders that frequently appear in the Middle East. An Image filtering and denoising were imposed in this work to enhance image quality and increase architecture performance. Experimental results revealed that MobileNet achieved the highest performance and accuracy among the CNN architectures and can classify disorder with high performance (95.7% accuracy). Future scope will focus more on proposing a new methodology for deep-based classification. In addition, we will expand the dataset for more images that consider new disorders and variations.

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“…We focus on these diagnoses since they are most common in the Middle East and Saudi Arabia. [29][30][31][32]…”

Section: Materials and Methods Datasetmentioning

confidence: 99%

Deep Learning Based Classification of Dermatological Disorders

AlSuwaidan

2023

Biomed Eng Comput Biol

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“…Most of the AD end up in remission, small number of childhood cases, especially severe ones persist into adulthood [4]. Studies show, that AD predispose to a higher risk of atopic and other than atopy comorbidities [5,6].…”

Section: Introductionmentioning

confidence: 99%

Investigating the role of Interleukin-33 and Soluble ST2 in pediatric atopic dermatitis

Paulauskaitė¹,

Eidukaitė²,

Rudzevičienė³

2020

Our Dermatol Online

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Background: Atopic dermatitis (AD) is a chronic, inflammatory skin disease, common in children. Pathologic cutaneous inflammation is driven by activated T-helper cells. Studies demonstrate that childhood AD is associated with Th2 immune activation. IL-33 is an intracellular cytokine, abundantly expressed in tissues, which are exposed to the environment. Cellular damage, due to scratching, encounter to infectious pathogens or exposure to allergens, trigger the release of IL-33. Extracellularly IL-33 acts as an activator for Th2 lymphocytes. Soluble ST2 (sST2) is a decoy receptor for IL-33. Combined to sST2, IL-33 loses its biological functions, which results in the alleviation of Th2 immune response. With this study we wanted to investigate the role of IL-33 and sST2 in pediatric AD. Material and Methods: Blood and stool samples from children with AD and healthy controls were tested for IL-33 and sST2 concentrations. Results: Children with AD presented significantly higher blood IL-33 concentrations, compared to healthy controls: 18,21 pg/ ml vs 0, p<0,05. Stool IL-33 levels demonstrated no significant difference between the two groups: 12,43 pg/ml vs 45,94 pg/ml, p>0,05. Blood and stool sST2 concentrations showed no significant difference: 67,58 pg/ml vs 74,96 pg/ ml, p>0,05; 0 vs 0, p>0,05, respectively. Conclusion: IL-33 is associated with pediatric AD. Blood, but not stool IL-33 testing can be used as a biomarker. sST2 showed no difference in AD.

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